Data Analysis Tasks(Level-2)

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Note: If you face any issues with file reading, please change the file_path variable based on the location of your data file on your system.

import pandas as pd
import matplotlib.pyplot as plt

file_path = r'C:\Users\Akshit Sajwan\Desktop\Cognifyz_Internship\Working Zone\DATA ANALYSIS TASK LIST AND DATASET\internship_tasks\dataset\dataset.csv'

df=pd.read_csv(file_path)
df.head()

	Restaurant ID	Restaurant Name	Country Code	City	Address	Locality	Locality Verbose	Longitude	Latitude	Cuisines	...	Currency	Has Table booking	Has Online delivery	Is delivering now	Switch to order menu	Price range	Aggregate rating	Rating color	Rating text	Votes
0	6317637	Le Petit Souffle	162	Makati City	Third Floor, Century City Mall, Kalayaan Avenu...	Century City Mall, Poblacion, Makati City	Century City Mall, Poblacion, Makati City, Mak...	121.027535	14.565443	French, Japanese, Desserts	...	Botswana Pula(P)	Yes	No	No	No	3	4.8	Dark Green	Excellent	314
1	6304287	Izakaya Kikufuji	162	Makati City	Little Tokyo, 2277 Chino Roces Avenue, Legaspi...	Little Tokyo, Legaspi Village, Makati City	Little Tokyo, Legaspi Village, Makati City, Ma...	121.014101	14.553708	Japanese	...	Botswana Pula(P)	Yes	No	No	No	3	4.5	Dark Green	Excellent	591
2	6300002	Heat - Edsa Shangri-La	162	Mandaluyong City	Edsa Shangri-La, 1 Garden Way, Ortigas, Mandal...	Edsa Shangri-La, Ortigas, Mandaluyong City	Edsa Shangri-La, Ortigas, Mandaluyong City, Ma...	121.056831	14.581404	Seafood, Asian, Filipino, Indian	...	Botswana Pula(P)	Yes	No	No	No	4	4.4	Green	Very Good	270
3	6318506	Ooma	162	Mandaluyong City	Third Floor, Mega Fashion Hall, SM Megamall, O...	SM Megamall, Ortigas, Mandaluyong City	SM Megamall, Ortigas, Mandaluyong City, Mandal...	121.056475	14.585318	Japanese, Sushi	...	Botswana Pula(P)	No	No	No	No	4	4.9	Dark Green	Excellent	365
4	6314302	Sambo Kojin	162	Mandaluyong City	Third Floor, Mega Atrium, SM Megamall, Ortigas...	SM Megamall, Ortigas, Mandaluyong City	SM Megamall, Ortigas, Mandaluyong City, Mandal...	121.057508	14.584450	Japanese, Korean	...	Botswana Pula(P)	Yes	No	No	No	4	4.8	Dark Green	Excellent	229

5 rows × 21 columns

Task 1: Restaurant Ratings

1. Analyze the distribution of aggregate ratings and determine the most common rating range.

2. Calculate the average number of votes received by restaurants.

df['Aggregate rating'].hist(bins=20, color='skyblue')
plt.title('Distribution of Aggregate Ratings')
plt.xlabel('Aggregate Rating')
plt.ylabel('Frequency')
plt.show()

rating_counts = pd.cut(df['Aggregate rating'], bins=5).value_counts()
print("Most common rating range:")
print(rating_counts)

Most common rating range:
Aggregate rating
(2.94, 3.92]       4590
(-0.0049, 0.98]    2148
(1.96, 2.94]       1430
(3.92, 4.9]        1380
(0.98, 1.96]          3
Name: count, dtype: int64

avg_votes = df['Votes'].mean()
print("Average number of votes received by restaurants:", avg_votes)

Average number of votes received by restaurants: 156.909747670401

Conclusion

• *Most common rating range:

Aggregate rating (2.94, 3.92] 4590 (-0.0049, 0.98] 2148 (1.96, 2.94] 1430 (3.92, 4.9] 1380 (0.98, 1.96] 3*

• Average number of votes received by restaurants: 156.909747670401

Task 2: Cuisine Combination

1. Identify the most common combinations of cuisines in the dataset.

2. Determine if certain cuisine combinations tend to have higher ratings.

cuisine_series = df['Cuisines'].dropna().str.split(', ')
cuisine_combinations = cuisine_series.apply(frozenset)
cuisine_combinations_counts = cuisine_combinations.value_counts()

top_cuisine_combinations = cuisine_combinations_counts.head(5)
print("Top 10 most common cuisine combinations:")
print(top_cuisine_combinations)

Top 10 most common cuisine combinations:
Cuisines
(North Indian)             936
(North Indian, Chinese)    616
(North Indian, Mughlai)    395
(Fast Food)                354
(Chinese)                  354
Name: count, dtype: int64

cuisine_combinations_avg_rating = df.groupby(cuisine_combinations)['Aggregate rating'].mean().dropna()
print("\nAverage rating for each cuisine combination:")
print(cuisine_combinations_avg_rating)

Average rating for each cuisine combination:
Cuisines
(Japanese, Desserts, French)                                    4.800000
(Nepalese)                                                      1.333333
(Pizza, Asian, North Indian, European, Italian, Continental)    4.100000
(Chinese, Thai, Continental)                                    3.650000
(American, Mexican, Finger Food)                                4.200000
                                                                  ...   
(Thai, Continental, North Indian, Malaysian, Chinese)           3.800000
(South Indian, Biryani)                                         1.500000
(Desserts, Fast Food, Indian, Continental)                      3.800000
(Fast Food, Cafe, Continental)                                  3.700000
(Italian, World Cuisine)                                        3.700000
Name: Aggregate rating, Length: 1339, dtype: float64

Conclusion

• Top 10 most common cuisine combinations Above the conclusion
• Average rating for each cuisine combination Above the conclusion

Task 3: Geographic Analysis

1. Plot the locations of restaurants on a map using longitude and latitude coordinates.

2. Identify any patterns or clusters of restaurants in specific areas.

import folium
from folium.plugins import MarkerCluster

m = folium.Map(location=[df['Latitude'].mean(), df['Longitude'].mean()], zoom_start=2)
marker_cluster = MarkerCluster().add_to(m)
for idx, row in df.iterrows():
    folium.Marker([row['Latitude'], row['Longitude']], 
                  popup=row['Restaurant Name']).add_to(marker_cluster)
m

Make this Notebook Trusted to load map: File -> Trust Notebook

import matplotlib.pyplot as plt
plt.figure(figsize=(10, 8))
plt.scatter(df['Longitude'], df['Latitude'], color='orange', s=5, alpha=0.5)
plt.title('Locations of Restaurants')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.show()

Conclusion

• The locations of restaurants on a map using longitude and latitude coordinates is generated by folium

• Identify any patterns or clusters of restaurants in specific area: It reveals areas with high restaurant density and helps identify patterns such as clustering in specific regions. High-density clusters may indicate popular urban centers or culinary hotspots.

Task 4: Restaurant Chains

1. Identify if there are any restaurant chains present in the dataset.
2. Analyze the ratings and popularity of different restaurant chains.

restaurant_chains = df['Restaurant Name'].value_counts()
top_restaurant_chains = restaurant_chains.head(10)
print("Top 10 restaurant chains:")
print(top_restaurant_chains)

Top 10 restaurant chains:
Restaurant Name
Cafe Coffee Day     83
Domino's Pizza      79
Subway              63
Green Chick Chop    51
McDonald's          48
Keventers           34
Pizza Hut           30
Giani               29
Baskin Robbins      28
Barbeque Nation     26
Name: count, dtype: int64

restaurant_chain_ratings = df.groupby('Restaurant Name')['Aggregate rating'].mean().dropna()
top_chain_ratings = restaurant_chain_ratings.loc[top_restaurant_chains.index]
top_chain_ratings.plot(kind='bar', color='skyblue')
plt.title('Average Ratings of Top 10 Restaurant Chains')
plt.xlabel('Restaurant Chain')
plt.ylabel('Average Rating')
plt.show()

Conclusion

• dataset contains multiple restaurant chains, with the top 10 chains having the highest number of outlets

• bar chart displaying the average ratings of the top 10 restaurant chains reveals the quality and customer satisfaction of each chain